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Despite the success of multilingual sequence-to-sequence pre-training, mostexisting approaches rely on document-level monolingual corpora in manydifferent languages, sentence-level bilingual corpora,\footnote{In this paper,we use `bilingual corpora' to denote parallel corpora with `bilingualtranslation pairs' in many different language pairs, each consisting of twosentences/documents with the same meaning written in different languages. Weuse `trilingual corpora' to denote parallel corpora with `trilingualtranslation pairs' in many different language combinations, each consisting ofthree sentences/documents.} and sometimes synthetic document-level bilingualcorpora. This hampers the performance with cross-lingual document-level taskssuch as document-level translation. Therefore, we propose to mine and leveragedocument-level trilingual parallel corpora to improve sequence-to-sequencemultilingual pre-training. We present \textbf{Tri}angular Document-level\textbf{P}re-training (\textbf{TRIP}), which is the first in the field toaccelerate the conventional monolingual and bilingual objectives into atrilingual objective with a novel method called Grafting. Experiments show thatTRIP achieves several strong state-of-the-art (SOTA) scores on threemultilingual document-level machine translation benchmarks and onecross-lingual abstractive summarization benchmark, including consistentimprovements by up to 3.11 d-BLEU points and 8.9 ROUGE-L points.

Advancing Multilingual Pre-training: TRIP Triangular Document-level Pre-training for Multilingual Language Models